Frequency Variability Research Articles

Frequency and Spatial Variability of European Record Floods

AbstractRegional envelope curves (RECs) have been used to characterize the flooding potential of regions worldwide. However, a comprehensive assessment for Europe is still missing. In this study we use the largest European flood database to quantify the magnitude of record floods, their dependence on catchment size, and the amount of available independent information, which is needed for estimatinig the REC's exceedance probability. We propose a framework for estimating REC parameters across large areas, consisting in partitioning the domain according to the observation density and estimating the REC slope through quantile regression. The results show that the REC parameters vary substantially across Europe. The envelope unit flood associated with a catchment area of 1,000 km2 varies between 0.1 and 6 m3s−1 km−2 and is highest in Mediterranean and Alpine areas and lowest in central‐eastern Europe. The slope of the envelope varies between 0 and −1 and is larger in Southern Europe than in Northern Europe. These differences are explained by steeper landscape in mountaineous regions and localized short duration storms producing large floods in small catchments in the Mediterranean, whose intensities are larger than in other parts of Europe. Based on the framework of probabilistic envelope curves we show that, despite the uneven observation density, the obtained RECs are associated with comparable exceedance probabilities.

Data Assimilation and Parameter Identification for Water Waves Using the Nonlinear Schrödinger Equation and Physics-Informed Neural Networks

The measurement of deep water gravity wave elevations using in situ devices, such as wave gauges, typically yields spatially sparse data due to the deployment of a limited number of costly devices. This sparsity complicates the reconstruction of the spatio-temporal extent of surface elevation and presents an ill-posed data assimilation problem, which is challenging to solve with conventional numerical techniques. To address this issue, we propose the application of a physics-informed neural network (PINN) to reconstruct physically consistent wave fields between two elevation time series measured at distinct locations within a numerical wave tank. Our method ensures this physical consistency by integrating residuals of the hydrodynamic nonlinear Schrödinger equation (NLSE) into the PINN’s loss function. We first showcase a data assimilation task by employing constant NLSE coefficients predetermined from spectral wave properties. However, due to the relatively short duration of these measurements and their possible deviation from the narrow-band assumptions inherent in the NLSE, using constant coefficients occasionally leads to poor reconstructions. To enhance this reconstruction quality, we introduce the base variables of frequency and wavenumber, from which the NLSE coefficients are determined, as additional neural network parameters that are fine tuned during PINN training. Overall, the results demonstrate the potential for real-world applications of the PINN method and represent a step toward improving the initialization of deterministic wave prediction methods.

Longitudinal Development of Holistic Formulaicity, Formulaic Sequences, and Lexical Complexity in Sojourner Diaries: A Dynamic Usage‐Based Perspective

AbstractBecause of authentic exposure, study‐abroad sojourners are expected to become more proficient in terms of holistic formulaicity (defined as targetlike language use of intensifiers, fillers, multiword sequences, lexical features, verb–argument constructions, pragmatic and discourse features, and so on), use of formulaic sequences, and lexical measures. This study traces the development of these constructs over time in written diary texts of 26 Catalan/Spanish bilingual sojourners in an Anglophone country during study abroad. It adopts a dynamic usage‐based perspective, underlining the importance of frequency of exposure and individual variability in developmental trajectories. Generalized additive mixed model analyses, which take individual nonlinear behavior into account, showed significant gains toward holistic formulaicity, but not in use of formulaic sequences nor in lexical complexity measures. We argue that at advanced stages some measures may have reached ceiling, but that sojourners may still progress in becoming more finely attuned to the conventionalized ways of saying things in the speech community.

Historical pyrodiversity in Douglas-fir forests of the southern Cascades of Oregon, USA

Our understanding of forest dynamics and successional pathways in coastal Douglas-fir (Pseudotsuga menziesii var menziesii) forests with relatively frequent mixed-severity fires is limited by a lack of annually precise dendroecological reconstructions that combine records of historical fires and tree establishment. The processes by which old-forest heterogeneity developed under historical fire regimes with recurrent low- and moderate-severity fires has not been well studied at fine temporal scales and across spatial scales. We developed crossdated multi-century records of fire and tree establishment histories in old forests (170 – 550 years) at 34 plots distributed across six sites. Study sites include warm-dry to cool-moist Douglas-fir forest types found in the southern west Cascades of Oregon, USA. Spatial variability in historical fire frequency and fire effects resulted in tremendous diversity in forest developmental histories, age structure, and forest conditions. Most historical fire intervals were very frequent (<10 years) to frequent (<25 years) in dry Douglas-fir forests. Exceptionally high fire frequency and an abrupt decrease in fire frequency after European colonization in dry Douglas-fir forests adds to growing evidence and recognition of Indigenous fire stewardship in montane Douglas-fir forests. In moist forests where Douglas-fir is seral to western hemlock, fire intervals were frequent to moderately frequent (<50 years), but intervals varied substantially over time. Relatively young moist forests burned frequently while mature moist forests had long fire intervals (50–160 years). Nearly all tree establishment cohorts were preceded by either stand-replacing (28 %) or non-stand-replacing fires (64 %). However, tree cohorts only provided evidence of 16 % of historical fire events that we reconstructed from cambial fire scars. This study demonstrates that frequent fire can be an important driver of forest development and in some contexts shapes the structure of coastal old-growth Douglas-fir forests, which are often characterized as developing from endogenous disturbances during long fire-free periods. The high level of pyrodiversity we observed was associated with variation in and interactions of micro-climate, topography, fuels, and Indigenous fire stewardship. We recommend rigorous dendroecological reconstructions across the coastal Douglas-fir region to refine our understanding of the geography of fire-mediated forest developmental dynamics in this important forest type, to inform forest management, conservation, and ecocultural restoration.

Detecting axion dark matter with black hole polarimetry

The axion, as a leading dark matter candidate, is the target of many ongoing and proposed experimental searches based on its coupling to photons. Ultralight axions that couple to photons can also cause polarization rotation of light which can be probed by the cosmic microwave background. In this work, we show that a large axion field is inevitably developed around black holes due to the Bose-Einstein condensation of axions, enhancing the induced birefringence effects. Therefore, measuring the modulation of supermassive black hole imaging polarization angles is a strong probe to the axion-photon coupling due to the formation of the axion condensation (axion star) which enhances the axion field. The oscillating axion field around black holes induces polarization rotation on the black hole image, which is detectable and distinguishable from astrophysical effects on the polarization angle, as it exhibits distinctive temporal variability and frequency invariability. In this work, we perform the theoretical calculation on the axion star formation rate and correspondingly the enhanced axion field value near supermassive black holes. Then, we present the range of axion-photon couplings within the axion mass range 10−21–10−16 eV that can be probed by the Event Horizon Telescope. The axion parameter space probed by black hole polarimetry will expand with the improvement in sensitivity on the polarization measurement and more black hole polarimetry targets with determined black hole masses. Published by the American Physical Society 2024

Green ammonia imports could supplement long-duration energy storage in the UK

Abstract There is growing recognition of the need for long-duration energy storage to cope with low frequency (i.e. seasonal to multi-annual) variability in renewable energy supplies. Recent analysis for the UK has estimated that 60–100 TWh of hydrogen storage could be required to provide zero-carbon backup for renewable energy supplies in 2050. However, the analysis did not consider the potential role of green energy imports as a supplement to domestic energy storage. Using a global spatially-explicit model of green hydrogen/ammonia production and shipping we estimate the lowest import costs for green ammonia to the UK, and compare them with the levelized costs of energy storage across scenarios of varying domestic renewable energy production. The results indicate that imported green ammonia could offer a cost-comparable alternative to domestic hydrogen production, storage and power generation, whilst increasing energy system resilience through supply diversification, at a similar or cheaper delivered energy cost compared to a hydrogen-only storage system. In countries lacking the geological potential for low-cost hydrogen storage, green ammonia imports could have an even more significant role.

Burn Care in the Street: A Survey of the Current Landscape of Burn Care Provided by Street Medicine Teams.

People experiencing homelessness are at increased risk for serious burn injuries and face additional barriers to care such as inability to perform wound care and difficulty with follow-up. Although not burn-specific, street medicine programs provide direct medical care to people experiencing unsheltered homelessness in their own environment and may be well positioned to bridge this gap in burn care for this population. We conducted a cross-sectional survey to characterize the burn care experience of street medicine teams with providing burn care for people experiencing homelessness. The 60 respondents included 18 (30%) physicians, 15 (25%) nurse practitioners or physician assistants, 15 (25%) registered nurses, and 6 (10%) medical students, and 6 (10%) other team members. The most common reported barriers to care were prior negative experiences with emergency departments, and transportation to burn centers. There was regional variability in frequency of providing burn care, which was reflected in respondent comfort with assessing and treating burns. Burns were most often dressed with daily dressings such as a non-adherent dressing over silver sulfadiazine or bacitracin. Silver-based contact dressings were rarely used. Street medicine teams in the United States are treating burn injuries among people experiencing homelessness, though management practices and experience treating burns was variable. As street medicine programs continue to grow, burn-related education, training, and connections to local burn centers for team members is important. Through strengthened partnerships between burn centers and street medicine teams, these programs may be well positioned to bridge the gap in burn care for people experiencing homelessness.

Unveiling the risks of ChatGPT in diagnostic surgical pathology.

ChatGPT, an AI capable of processing and generating human-like language, has been studied in medical education and care, yet its potential in histopathological diagnosis remains unexplored. This study evaluates ChatGPT's reliability in addressing pathology-related diagnostic questions across ten subspecialties and its ability to provide scientific references. We crafted five clinico-pathological scenarios per subspecialty, simulating a pathologist using ChatGPT to refine differential diagnoses. Each scenario, aligned with current diagnostic guidelines and validated by expert pathologists, was posed as open-ended or multiple-choice questions, either requesting scientific references or not. Outputs were assessed by six pathologists according to. (1) usefulness in supporting the diagnosis and (2) absolute number of errors. We used directed acyclic graphs and structural causal models to determine the effect of each scenario type, field, question modality, and pathologist evaluation. We yielded 894 evaluations. ChatGPT provided useful answers in 62.2% of cases, and 32.1% of outputs contained no errors, while the remaining had at least one error. ChatGPT provided 214 bibliographic references: 70.1% correct, 12.1% inaccurate, and 17.8% non-existing. Scenario variability had the greatest impact on ratings, and latent knowledge across fields showed minimal variation. Although ChatGPT provided useful responses in one-third of cases, the frequency of errors and variability underscores its inadequacy for routine diagnostic use and highlights the need for discretion as a support tool. Imprecise referencing also suggests caution as a self-learning tool. It is essential to recognize the irreplaceable role of human experts in synthesizing images, clinical data, and experience for the intricate task of histopathological diagnosis.

Enhancing the variability of simulated random processes using the spectral representation method

In this paper, the traditional spectral representation method for simulating stochastic processes is revisited. The main goal is to introduce various approaches to enhance the variability of the generated sample functions to better reflect variabilities observed in field measurements and experimental data, while keeping the ensemble-averaged power spectra unchanged. The general idea, gradually developed over the last decade, is to randomize the power spectral density function of the uncertain physical quantities by modeling certain parameters as random variables calibrated from experimental data. The potential of such approaches has not been fully explored yet. This work explores the pros and cons of using power spectral density functions with random parameters in the simulation of highly variable, stationary and nonstationary, Gaussian stochastic processes. The main characteristics of a stochastic process, such as distribution of maxima and variability in time and frequency, are examined. The use of a pass-band Butterworth filter to control the variability of the Fourier spectrum of the simulated samples is studied. The Butterworth filter is selected so that the ensemble-averaged power spectral density matches the target power spectrum, but each individual generated sample function possesses different amplitude and frequency distributions. Comparisons between samples generated using the traditional spectral representation method and those generated with enhanced variability are made through a series of illustrative examples. Results show that with the proposed enhanced-variability spectral representation method, certain characteristics of the stochastic process, such as the distribution of maxima, will be significantly different (while the ensemble-averaged power spectra will remain the same). The implications in reliability studies are obvious.

Breathing variability during running in athletes: The role of sex, exercise intensity and breathing reserve

Highly trained aerobic athletes progressively use most of their breathing reserve with increased exercise intensity during whole-body exercise. Additionally, females typically present proportionally smaller lungs than males. Therefore, sex, exercise intensity, and breathing reserve use likely influence the volume and time in which respiratory parameters vary between consecutive breaths during whole-body exercise. However, breath-by-breath variability has been scarcely investigated during exercise. Accordingly, we sought to investigate breath-by-breath pulmonary ventilation (V̇E), tidal volume (VT), and respiratory frequency (fR) variability during a maximal treadmill incremental exercise test in 17 females and 18 males highly trained professional endurance runners. The breath-by-breath variability was analyzed by root mean square of successive differences (RMSSD) within 1-minute windows. Females had lower absolute and percent predicted forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) than males, as well as lower height-adjusted absolute FVC than males. V̇E and VT reserve use were similar between the sexes at peak exercise. While RMSSDV̇E and RMSSDfR did not change over exercise (P > 0.05), RMSSDVT progressively decreased (P < 0.001). RMSSDVT was negatively correlated with VT reserve use only in males. Females showed lower RMSSDV̇E than males during the entire exercise test (P < 0.001). At iso-V̇E reserve use, between-sex differences in RMSSDV̇E persisted (P = 0.003). Our findings indicate that exercise intensity decreases VT variability in professional runners, which is linked to VT reserve use in males but not females. Additionally, the female sex lowers V̇E variability regardless of exercise intensity and V̇E reserve use.

Association between loneliness, social isolation, and frailty: A two-sample Mendelian randomization

Abstract Objective: This study aimed to examine the causal relationship between loneliness, social isolation, and frailty using Mendelian Randomization (MR) analysis. Methods: Data were obtained from a large-scale genome-wide association study (GWAS) of a European-descent population. Variables included frailty index (FI), 2 loneliness variables (Feeling lonely, able to confide), and 3 social isolation variables (frequency of friend/family visits, number in household, no leisure/social activities). The inverse variance weighting (IVW) method was set as the primary MR analysis, with MR-Egger and weighted median methods employed as complementary approaches. Cochran Q test, the MR-Egger intercept test, and MR-PRESSO methods were used to evaluate the robustness of MR results. Bonferroni corrected P-values &lt;0.01 were considered statistically significant. Results: Genetic predisposition to feeling lonely [IVW: odds ratio (OR)=1.47; 95% CI: 1.34–1.60; P&lt;0.001] and no leisure or social activities (IVW: OR=1.78; 95% CI: 1.50–2.11; P&lt;0.001) were significantly associated with increased FI. In addition, the genetically predicted ability to confide was positively associated with decreased FI (IVW: OR=0.91; 95% CI: 0.87–0.95; P&lt;0.001). There was no statistically significant causal association between the number in the household, frequency of friend/family visits, and the risk of frailty. Conclusions: This MR study provides evidence of the causal relationship between loneliness, social isolation, and frailty from a genetic perspective and highlights the importance of decreasing social isolation in the development of frailty prevention and intervention programs.

Physiotherapy Outcomes Are Associated With Shorter Waiting Times, More Treatment Sessions and Younger Age: Analysis of a Clinical Database.

Musculoskeletal (MSK) conditions affect over 20.3 million people in the UK, presenting a substantial economic impact on health and social services. Physiotherapy can alleviate MSK conditions, especially if delivered in the acute or sub-acute period. However, patients often present after significant waiting times. Our analysis examined how waiting times and the number of treatments influenced physiotherapy outcomes for MSK conditions. Retrospective analysis of the Data for Impact, Physio First dataset. Logistic regression models assessed the effects of symptom duration, treatment frequency, and other variables on pain, Patient-Specific Functional Scores (PSFSs), and Goal Achievement (GA). Analysis of 15,624 patient records showed that patients treated within two weeks of symptom onset were more likely to have favourable outcomes in pain (odds ratio [OR]=2.01, 95% Confidence Interval [95% CI]=1.65-2.45), PSFS (OR=1.80, 95% CI=1.55-2.08), and GA (OR=1.74, 95% CI=1.51-2.01) compared to those treated after longer durations. Receiving four or more treatment sessions significantly improved outcomes compared with only one session (pain: OR=4.64, PSFS: OR=5.72, GA: OR=1.94, all p's<0.001), with no additional benefits beyond four sessions. Younger age was associated with better outcomes (approximately OR=0.99 per year age difference). Other findings included better outcomes in males and in those with fewer previous episodes of the condition. Shorter waiting times, a greater number of treatments, and younger patient age are associated with better physiotherapy outcomes for MSK conditions.

Tourism and uncertainty: a machine learning approach

ABSTRACT In this paper, we attempt to create a unique forecasting model to forecast out-of-sample the tourism demand in 24 European Union countries. The initial dataset included 34 relevant variables of annual frequency that span the period from 2010 to 2020 for 40 countries. A data prefiltering process resulted in a final set of 17 relevant variables for 24 countries. Additionally, in the effort to investigate the impact of uncertainty on international tourism, apart from the traditional factors that affect tourism, we also include variables that measure various forms of uncertainty: we use the World Pandemic Uncertainty (WPU) Index, the Global CBOE Volatility Index, the Political Globalisation Index, the Economic Globalisation Index, and the Political Stability Index. In the empirical part of our research, we employ and compare in terms of their forecasting accuracy a set of six state-of-the-art machine learning algorithms, the Support Vector Regression with both a linear and an RBF kernel, the Random Forests, the Decision Trees, the KNN, and gradient-boosting trees. The results show that the Gradient-Boosting Trees algorithm outperforms the other five models providing the most accurate forecasts with a MAPE of 0.10% and 1.36% in the training and the out-of-sample tests, respectively.

Variability in monthly occurrence frequency of internal waves in the East Vietnam Sea

Variability in monthly occurrence frequency of internal waves in the East Vietnam Sea

Distinct Interannual Variability and Physical Mechanisms of Snowfall Frequency over the Eurasian Continent during Autumn and Winter

Distinct Interannual Variability and Physical Mechanisms of Snowfall Frequency over the Eurasian Continent during Autumn and Winter

Digital phenotypes of real-time suicidal ideation: Correlates and consequences.

Suicidal ideation variability refers to within-day fluctuations in suicidal ideation, and has recently been proposed as an indicator of suicide risk. However, not much is known yet about its correlates and clinical relevance. We examined characteristics of real-time suicidal ideation using Ecological Momentary Assessment in 82 individuals with current active suicidal ideation. Data were collected four times daily over 21 days. Latent profile analysis was used to identify subtypes of suicidal ideation. We further examined sociodemographic and clinical correlates of the profiles, and their association with the occurrence of suicide attempts during a 1-year follow-up. We identified three "digital" phenotypes of suicidal ideation that differed on the frequency, intensity and variability of ideation. The profiles were: high frequency, high intensity, moderate variability (Phenotype 1), moderate/high frequency, moderate intensity, high variability (Phenotype 2), and moderate frequency, low intensity, low variability (Phenotype 3). Phenotypes 1 and 2 were associated with a worse clinical profile at baseline (higher suicidal ideation and depressive symptom severity), and increased odds of suicide attempt during follow-up, compared to Phenotype 3. Phenotype 1 was further characterized by repeated suicidal behavior. Two phenotypes of real-time suicidal ideation were identified that appear to confer a higher risk of suicidal behavior in the near future (12 months). These phenotypes were characterized by higher variability of suicidal ideation-and also higher intensity and frequency of ideation. Considering the small sample size, the clinical usefulness of the profiles remains to be demonstrated.

High frequency variability index in predicting postoperative pain in video/robotic-assisted thoracoscopic surgery under combined general anesthesia and peripheral nerve block: an observational study.

The high frequency variability index (HFVI)/analgesia nociception index (ANI) is purported to assess the balance between nociception and analgesia in patients under general anesthesia. This observational study investigated whether intraoperative HFVI/ANI correlates with postoperative pain in patients performed with nerve block under general anesthesia in video/robotic-assisted thoracoscopic surgery (VATS/RATS). We investigated whether maximum postoperative pain at rest and postoperative morphine consumption are associated with HFVI/ANI just before extubation, mean HFVI/ANI during anesthesia, the difference in HFVI/ANI between before and 5min after the start of surgery, and the difference in HFVI/ANI between before and 5min after the nerve block. Data obtained from 48 patients were analyzed. We found no significant association between HFVI/ANI just before extubation and postoperative Numerical Rating Scale (NRS) score. Receiver operating characteristic curve analysis revealed that moderate (NRS > 3) or severe (NRS > 7) postoperative pain could not be predicted by HFVI/ANI just before extubation. In addition, there were no associations between postoperative morphine consumption and HFVI/ANI at any time points. The present study demonstrated that it is difficult to predict the degree of postoperative pain in patients undergoing VATS/RATS under general anesthesia combined with peripheral nerve block, by using HFVI/ANI obtained at multiple time points during general anesthesia.

Sign duration and signing rate in British Sign Language, Dutch Sign Language and Swedish Sign Language

In this article, we look at sign duration and signing rate in corpora of three sign languages – British Sign Language (BSL), Dutch Sign Language (NGT), and Swedish Sign Language (STS). We investigate whether token frequency and sociolinguistic variables (e.g., age, gender, region) influence the production rate of signing. Following Zipf’s law of abbreviation, we see that a sign’s duration is negatively correlated with its frequency. Both sign duration and signing rate are found to correlate with signer age, in that older signers have longer durations and lower rates than younger signers. Signers' gender, family (deaf or hearing), and age of exposure have no effect on duration or signing rate. For NGT and STS, there is no effect of region on either duration or rate. However, in the BSL data, duration and signing rate vary with region. The overall findings align with previous work on spoken languages, particularly that frequency and aging are correlated with word length and production rate, thus demonstrating such patterns across modalities of language.

Data-Driven Optimization of Plasma Electrolytic Oxidation (PEO) Coatings with Explainable Artificial Intelligence Insights

PEO constitutes a promising surface technology for the development of protective and functional ceramic coatings on lightweight alloys. Despite its interesting advantages, including enhanced wear and corrosion resistances and eco-friendliness, the industrial implementation of PEO technology is limited by its relatively high energy consumption. This study explores the development and optimization of novel PEO processes by means of machine learning (ML) to improve the coating thickness. For this purpose, ML models random forest and XGBoost were employed to predict the thickness of the developed PEO coatings based on the key process variables (frequency, current density, and electrolyte composition). The predictive performance was significantly improved by including the composition of the used electrolyte in the models. Furthermore, Shapley values identified the pulse frequency and the TiO2 concentration in the electrolyte as the most influential variables, with higher values leading to increased coating thickness. The residual analysis revealed a certain heteroscedasticity, which suggests the need for additional samples with high thickness to improve the accuracy of the model. This study reveals the potential of artificial intelligence (AI)-driven optimization in PEO processes, which could pave the way for more efficient and cost-effective industrial applications. The findings achieved further emphasize the significance of integrating interactions between variables, such as frequency and TiO2 concentration, into the design of processing operations.

DOSage of Exercise for chronic low back pain disorders (DOSE): protocol for a systematic review with dose-response network meta-analysis

Chronic low back disorders are the leading cause of direct and indirect healthcare burden globally. Exercise training improves pain intensity, mental health and physical function. However, the optimal prescription variables...